PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to…

By C. Granier, L. Aguirrezábal, Karine Chenu, S. Cookson, M. Dauzat, P. Hamard, J. Thioux, Gaëlle Rolland, Sandrine Bouchier-Combaud, A. Lebaudy and 3 more. 2006

555 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Plant Science, Computational Biology & Bioinformatics

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Citations in the corpus

Finely-grained annotated datasets for image-based plant phenotyping (2016, methodology): “In the last decades several approaches have been proposed to measure visual traits of plants in an automated fashion (Granier et al., 2006; Walter et al., 2007; Jansen et al., 2009; Nagel et al., 2012) together with customized image processing pipelines to analyze aspects of the acquired image data…”
Dissecting the Phenotypic Components of Crop Plant Growth and Drought Responses Based on High-Throughput Image Analysis[W][OPEN] (2014, background): “…plant phenotyping platforms have been developed recently and are applied to investigate plant performance under different environments (Granier et al., 2006; Walter et al., 2007; Biskup et al., 2009; Jansen et al., 2009; Arvidsson et al., 2011; Golzarian et al., 2011; Nagel et al.,…”
Image-based plant phenotyping with incremental learning and active contours (2014, background): “…field of view requires that images are acquired with each plant in isolation and usually involve some robotic (or manual) placement of the plant (e.g., Hartmann et al., 2011) or the camera (e.g., Granier et al., 2006; Walter et al., 2007), which lowers throughput or increases equipment cost.”
Annotated Image Datasets of Rosette Plants (2014, background): “In the last decades a variety of approaches based on images have been developed to measure visual traits of plants in an automated fashion [1, 2, 3].”
A Review of Imaging Techniques for Plant Phenotyping (2014, background): “In one example for shoot biomass in a controlled environment, the projected leaf area in plants such as Arabidopsis thaliana and maize is available through commercial systems [7,24] that are based on visible imaging.”
Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype–phenotype relationships and its relevance to crop improvement (2013, background): “…balance in hydroponics (Rengasamy 2010; Tavakkoli et al. 2010) to conveyer systems in glasshouses with digitally controlled irrigation systems (Granier et al. 2006; Jansen et al. 2009; Neumann 2013; Pereyra-Irujo et al. 2012) to the use of rainout shelters in the field (Czyczyło-Mysza et al.…”
HTPheno: An image analysis pipeline for high-throughput plant phenotyping (2011, methodology): “A technology called PHENOPSIS [1] developed by Optimalog is used by the French National Institute for Agricultural Research (INRA) for Arabidopsis thaliana.”
Drought Resistance Improvement in Rice: An Integrated Genetic and Resource Management Strategy (2011, background): “・The development of repeatable and predictive field screening methods suitable for use in breeding programs, and the prospects for new high-throughput and precise phenotyping under drought (Campos et al., 2004; Granier et al., 2006; Serraj and Cairns, 2006).”
segmentation in plant phenotyping: a collation study. Machine Vision and Applications (2017, background): “While previously such phenotypes were annotated manually by experts, recently image-based nondestructive approaches are gaining attention among plant researchers to measure and study visual phenotypes of plants [24,28,41,55].”
Model-assisted phenotyping and ideotype design (2015, background): “To study the response of specific traits in detail, high-throughput phenotyping platforms have recently been established in greenhouses or in the field, allowing fine environmental monitoring (e.g. Granier et al., 2006; Furbank and Tester, 2011).”
Plant phenotyping: from bean weighing to image analysis (2015, methodology): “Such methods have been successfully applied in the laboratory to assess the performance of Arabidopsis [68], tobacco [69] or cereal grain crops [11,12].”
Comprehending crystalline β-carotene accumulation by comparing engineered cell models and the natural carotenoid-rich system of citrus (2011, unknown): “The ‘PHENOPSIS’ system was used to automate the maintenance of soil water content at predetermined levels across different Arabidopsis accessions, thereby enabling the identification of an Arabidopsis accession altered in its response to water deficit (Granier et al., 2006).”